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ColossalAI/colossalai/utils/profiler/mem_profiler.py

51 lines
1.6 KiB

from pathlib import Path
from typing import Union
from colossalai.engine import Engine
from torch.utils.tensorboard import SummaryWriter
from colossalai.engine.ophooks import MemTracerOpHook
from colossalai.utils.profiler import BaseProfiler
class MemProfiler(BaseProfiler):
"""Wraper of MemOpHook, used to show GPU memory usage through each iteration
To use this profiler, you need to pass an `engine` instance. And the usage is same like
CommProfiler.
mm_prof = MemProfiler(engine)
with ProfilerContext([mm_prof]) as prof:
writer = SummaryWriter("mem")
engine.train()
...
prof.to_file("./log")
prof.to_tensorboard(writer)
"""
def __init__(self, engine: Engine, warmup: int = 50, refreshrate: int = 10) -> None:
super().__init__(profiler_name="MemoryProfiler", priority=0)
self._mem_tracer = MemTracerOpHook(warmup=warmup, refreshrate=refreshrate)
self._engine = engine
def enable(self) -> None:
self._engine.add_hook(self._mem_tracer)
def disable(self) -> None:
self._engine.remove_hook(self._mem_tracer)
def to_tensorboard(self, writer: SummaryWriter) -> None:
stats = self._mem_tracer.async_mem_monitor.state_dict['mem_stats']
for info, i in enumerate(stats):
writer.add_scalar(
"memory_usage/GPU",
info,
i
)
def to_file(self, data_file: Path) -> None:
self._mem_tracer.save_results(data_file)
def show(self) -> None:
stats = self._mem_tracer.async_mem_monitor.state_dict['mem_stats']
print(stats)